{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f954fd0-fb3e-4f8c-a18e-e3cc24a33282",
   "metadata": {},
   "source": [
    "### 预备知识"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b8fd54f-309d-4976-b5c3-b7f458a15b5f",
   "metadata": {},
   "source": [
    "#### 同比和环比"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2c7ba36-a88d-4df9-b31a-ff5946a71b81",
   "metadata": {},
   "source": [
    "同比和环比是两个常见的统计术语。\n",
    " \n",
    "- 同比是指本年第n月与过去某年的第n月比，例如，2024年2月份和2023年2月份相比。它的主要作用是消除季节变动的影响，用以说明本期发展水平与同期比较的相对发展速度。\n",
    "\n",
    "- 设本期数为A，同期数为B，同比增长率 =（A - B）÷ B×100%。例如，今年销售额是120万元，去年销售额是100万元，那么同比增长率 =（120 - 100）÷ 100×100% = 20%。\n",
    " \n",
    "- 环比是指连续2个统计周期内的量的变化比。比如，用2024年2月份的数据和2024年1月份的数据相比。环比主要是反映数据的短期变化。\n",
    "\n",
    "- 设本期数为M，上期数为N，环比增长率 =（M - N）÷ N×100%。例如，这个月的产量是550件，上个月产量是500件，环比增长率 =（550 - 500）÷ 500×100% = 10%。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b546a8e-275c-45fd-881b-5cca38bde4e9",
   "metadata": {},
   "source": [
    "#### 商品分类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "259e3929-317c-4773-a76c-0fd0bd196e91",
   "metadata": {},
   "source": [
    "按功能用途分大类\n",
    " \n",
    "- 比如服装类，这个大类主要用于遮体、保暖和装饰。在服装这个大类下可以细分出小类，像上衣、裤子、裙子等。上衣还能再细分T恤、衬衫、毛衣等小类。\n",
    "- 再如家居用品类是一个大类，包括家具、家纺等小类。家具又可以细分为床、沙发、桌椅等更小的类别。\n",
    " \n",
    "按消费人群分大类\n",
    " \n",
    "- 有儿童用品大类，包含儿童玩具、儿童服装、儿童食品等小类。儿童玩具小类下还可以细分积木、玩偶、遥控玩具等。\n",
    "- 还有成人用品大类，像办公用品小类就属于成人用品大类，办公用品还能再分为笔、本子、文件夹等更细致的小类。\n",
    " \n",
    "按原材料分大类\n",
    " \n",
    "- 例如塑料制品是一个大类，它的小类可以包括塑料容器、塑料玩具、塑料管材等。塑料容器小类还可以分为塑料水杯、塑料收纳盒等。\n",
    "- 纺织品也是大类，可分为棉织品、麻织品、丝织品等小类。棉织品下还能细分纯棉T恤、纯棉床单等。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d39af3-8e25-4cc0-be27-604a896d7d94",
   "metadata": {},
   "source": [
    "#### 分析举栗"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4784501-f421-4c57-b4d7-776ee13a1686",
   "metadata": {},
   "source": [
    "<img src='img/01.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e063f209-dd2b-41c6-ba01-272a93a55549",
   "metadata": {},
   "source": [
    "通过奶粉产品的净含量和各年龄段婴幼儿奶粉推荐食用量的估算，我们可以得到以下理论值数据(下面的表是错误的表)：\n",
    "<img src='img/02.jpg'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaed9129-73b4-4080-9e57-d745df3e5477",
   "metadata": {},
   "source": [
    "从上表中可以看出，用量最大的4段全年龄段理论上最多食用110罐400g的奶粉，一次性购买194罐以上才算异常值明显不合理。另外翻查2014年婴幼儿奶粉相关调研报告，400g的奶粉产品均价在250元左右，一次性购买100罐400g奶粉需要2万5千元，对于任何家庭来说，都是不合理支出结构。\n",
    "\n",
    "因此，**衡量异常值，不能仅通过统计学意义，还需要结合业务的实际情况。**\n",
    "\n",
    "在没有内部业务数据支撑下，以行业报告作为补充对异常值进行划分。根据国双2018年本土婴幼儿奶粉电商消费研究的数据，在电商平台购买婴幼儿奶粉的消费者年均购买次数约为27次，“双十一”、“618”两个购物节是囤货高峰。\n",
    "\n",
    "婴幼儿在0-1岁时，理论上一共需要81罐400g奶粉，假设用户除“双十一”、“618”外其他时间每次只购买1罐，那么两个购物节平均需要承担27罐奶粉，向上取整后，以单笔销量超过30罐奶粉作异常值处理。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18180d6d-e583-4dae-b8c3-aef8eff8dcdc",
   "metadata": {},
   "source": [
    "#### 多维度分析技能"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f03bd443-40d6-498d-90f7-32e0ec5d43c2",
   "metadata": {},
   "source": [
    "多维度的分析，应该是一个金字塔式的分析路径：从一个维度的整体到局部，再引入另外一个维度的整体再到局部，而不是在多个维度间反复横跳。\n",
    "\n",
    "初步规划的分析路径如下：\n",
    "\n",
    "- 1 观察各年度每月销量情况走势\n",
    "- 2 2015年1-2月的销量走势对比13年和14年，判断销量的好或差？\n",
    "- 3 如果销量差，问题出在什么地方\n",
    "- 4 如果销量差，还有多少缺口，有多少时间挽救，重要的挽救时间节点是什么时候？\n",
    "- 5 如果要冲销量，推广什么品类？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2159a12c-22bb-4c5e-ae8e-46518a10ad56",
   "metadata": {},
   "source": [
    "https://tianchi.aliyun.com/competition/entrance/532082/information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc060596-be5d-40f0-97ba-c256cab0e490",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sns.set()\n",
    "sys.path.append('..')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adad528b-5fd3-4111-a5a8-fd79086f92ae",
   "metadata": {},
   "source": [
    "### 数据探索与清洗阶段"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94d6db65-a57a-4ec4-b4fd-5afa9c33bef6",
   "metadata": {},
   "source": [
    "**订单信息**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbcb658f-c1c8-49b4-9df7-c0e0b9910e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>auction_id</th>\n",
       "      <th>category_2</th>\n",
       "      <th>category_1</th>\n",
       "      <th>buy_mount</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>20141103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id   auction_id  category_2  category_1  buy_mount       day\n",
       "0  786295544  41098319944    50014866    50022520          2  20140919\n",
       "1  532110457  17916191097    50011993          28          1  20131011\n",
       "2  249013725  21896936223    50012461    50014815          1  20131011\n",
       "3  917056007  12515996043    50018831    50014815          2  20141023\n",
       "4  444069173  20487688075    50013636    50008168          1  20141103"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_df = pd.read_csv('tianchi_mum_baby_trade_history.csv')\n",
    "trade_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d956d270-db60-426a-99ec-be3c851eb341",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 29971 entries, 0 to 29970\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype\n",
      "---  ------      --------------  -----\n",
      " 0   user_id     29971 non-null  int64\n",
      " 1   auction_id  29971 non-null  int64\n",
      " 2   category_2  29971 non-null  int64\n",
      " 3   category_1  29971 non-null  int64\n",
      " 4   buy_mount   29971 non-null  int64\n",
      " 5   day         29971 non-null  int64\n",
      "dtypes: int64(6)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "trade_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346b27c0-2b44-4d0e-9695-330770d9a9e7",
   "metadata": {},
   "source": [
    "`\n",
    "销量数据的异常值是整一个分析当中影响最大的，数据录入错误或运营的刷单等行为，会让个别时间段的销量猛增，严重影响趋势的判断，因此先剔除销量中的异常值。\n",
    "`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "70bcc8c2-f4f8-4bc3-aa7e-494382dd063a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max    20150205\n",
       "min    20120702\n",
       "Name: day, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_df['day'].agg(['max','min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "83d204ed-564f-4799-a44c-26f2e297ea73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    29971.000000\n",
       "mean         2.544126\n",
       "std         63.986879\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          1.000000\n",
       "75%          1.000000\n",
       "max      10000.000000\n",
       "Name: buy_mount, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_df['buy_mount'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd34116-b910-4ab5-ab21-7440f21cdfdc",
   "metadata": {},
   "source": [
    "看起来，直接查看descrbe是没什么意义的，先按照大类分组以后进行查看by_mount的异常值；**衡量异常值，不能仅通过统计学意义，还需要结合业务的实际情况。**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8133e00c-f8d7-406c-a275-bbc200b3f606",
   "metadata": {},
   "source": [
    "上面的观点是和鲸社区创造者的观点(具体看readme里面的url)，但是个人认为，在具体分析之前，使用统计指标进行数据的除筛是很关键的。可以先筛除超过中位数$3\\sigma$的数据。同时可以按照购买总量和频率排除异常值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a41753c-a415-4e06-ae37-8cec7953ac65",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 29941 entries, 0 to 29970\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype\n",
      "---  ------      --------------  -----\n",
      " 0   user_id     29941 non-null  int64\n",
      " 1   auction_id  29941 non-null  int64\n",
      " 2   category_2  29941 non-null  int64\n",
      " 3   category_1  29941 non-null  int64\n",
      " 4   buy_mount   29941 non-null  int64\n",
      " 5   day         29941 non-null  int64\n",
      "dtypes: int64(6)\n",
      "memory usage: 1.6 MB\n"
     ]
    }
   ],
   "source": [
    "max_buy_mount = 3*63.986879 + 1\n",
    "trade_df = trade_df[trade_df['buy_mount']<max_buy_mount]\n",
    "trade_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2f66d1fd-b183-4d54-b67a-d0b78fc23fc0",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "         count  sum\n",
       "user_id            \n",
       "2356         1    1\n",
       "2757         1    1\n",
       "3942         1    1\n",
       "4468         1    1\n",
       "7164         1    1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "temp_df = trade_df.groupby('user_id')['buy_mount'].agg(['count','sum'])\n",
    "temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ba98954b-fdd0-4284-a9c4-e34021fc696f",
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       "    <tr>\n",
       "      <th>53329934</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2356452983</th>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518609126</th>\n",
       "      <td>1</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894938964</th>\n",
       "      <td>1</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352340432</th>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877513481</th>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392530596</th>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22126987</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23821747</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25944466</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60966055</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66105351</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72432679</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73559800</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98953057</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count  sum\n",
       "user_id               \n",
       "694223139       1  192\n",
       "200829566       1  176\n",
       "359601689       1  160\n",
       "96977512        1  159\n",
       "1756069759      1  151\n",
       "53329934        1  150\n",
       "2356452983      1  148\n",
       "518609126       1  133\n",
       "1894938964      1  133\n",
       "352340432       1  110\n",
       "877513481       1  110\n",
       "392530596       1  101\n",
       "22126987        1  100\n",
       "23821747        1  100\n",
       "25944466        1  100\n",
       "60966055        1  100\n",
       "66105351        1  100\n",
       "72432679        1  100\n",
       "73559800        1  100\n",
       "98953057        1  100"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df.sort_values(['sum','count'],ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8034956-8654-4b22-8dbf-96419e4d7f56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    29941.000000\n",
       "mean         1.662536\n",
       "std          5.145759\n",
       "min          1.000000\n",
       "10%          1.000000\n",
       "20%          1.000000\n",
       "30%          1.000000\n",
       "40%          1.000000\n",
       "50%          1.000000\n",
       "60%          1.000000\n",
       "70%          1.000000\n",
       "80%          1.000000\n",
       "90%          2.000000\n",
       "95%          3.000000\n",
       "99%         12.000000\n",
       "99.5%       24.000000\n",
       "99.6%       30.000000\n",
       "99.7%       40.000000\n",
       "99.8%       52.480000\n",
       "99.9%      100.000000\n",
       "max        192.000000\n",
       "Name: buy_mount, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_df['buy_mount'].describe([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.95,0.99,0.995,0.996,0.997,0.998,0.999])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a1514ff-9d3e-481c-b643-3c566d68ea5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 29811 entries, 0 to 29970\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype\n",
      "---  ------      --------------  -----\n",
      " 0   user_id     29811 non-null  int64\n",
      " 1   auction_id  29811 non-null  int64\n",
      " 2   category_2  29811 non-null  int64\n",
      " 3   category_1  29811 non-null  int64\n",
      " 4   buy_mount   29811 non-null  int64\n",
      " 5   day         29811 non-null  int64\n",
      "dtypes: int64(6)\n",
      "memory usage: 1.6 MB\n"
     ]
    }
   ],
   "source": [
    "trade_df = trade_df[trade_df['buy_mount']<30]\n",
    "trade_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "056d72a3-2346-432b-9e21-587c8b4184f6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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      "text/plain": [
       "     user_id   auction_id  category_2  category_1  buy_mount       day\n",
       "0  786295544  41098319944    50014866    50022520          2  20140919\n",
       "1  532110457  17916191097    50011993          28          1  20131011\n",
       "2  249013725  21896936223    50012461    50014815          1  20131011\n",
       "3  917056007  12515996043    50018831    50014815          2  20141023\n",
       "4  444069173  20487688075    50013636    50008168          1  20141103"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c8b9c980-e3f3-4590-aa98-66ef1e18267a",
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   "outputs": [
    {
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "     user_id   auction_id  category_2  category_1  buy_mount        day\n",
       "0  786295544  41098319944    50014866    50022520          2 2014-09-19\n",
       "1  532110457  17916191097    50011993          28          1 2013-10-11\n",
       "2  249013725  21896936223    50012461    50014815          1 2013-10-11\n",
       "3  917056007  12515996043    50018831    50014815          2 2014-10-23\n",
       "4  444069173  20487688075    50013636    50008168          1 2014-11-03"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_df.day = trade_df.day.map(lambda x:pd.to_datetime(str(x)))\n",
    "trade_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa1acf88-1544-4f8e-82d9-d8fa43d89ff4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>50013636</td>\n",
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       "      <td>1</td>\n",
       "      <td>2014-11-03</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id   auction_id  category_2  category_1  buy_mount        day     Y  \\\n",
       "0  786295544  41098319944    50014866    50022520          2 2014-09-19  2014   \n",
       "1  532110457  17916191097    50011993          28          1 2013-10-11  2013   \n",
       "2  249013725  21896936223    50012461    50014815          1 2013-10-11  2013   \n",
       "3  917056007  12515996043    50018831    50014815          2 2014-10-23  2014   \n",
       "4  444069173  20487688075    50013636    50008168          1 2014-11-03  2014   \n",
       "\n",
       "    M  \n",
       "0   9  \n",
       "1  10  \n",
       "2  10  \n",
       "3  10  \n",
       "4  11  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_df['Y'] = trade_df.day.apply(lambda x:x.year)\n",
    "trade_df['M'] = trade_df.day.apply(lambda x:x.month)\n",
    "trade_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c4bc0a-2594-4bf7-9516-8269f056daf1",
   "metadata": {},
   "source": [
    "**用户信息**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6e32336a-b146-4f13-8df6-28c2752cb393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>birthday</th>\n",
       "      <th>gender</th>\n",
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       "  <tbody>\n",
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       "      <td>415971</td>\n",
       "      <td>20121111</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1372572</td>\n",
       "      <td>20120130</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10339332</td>\n",
       "      <td>20110910</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10642245</td>\n",
       "      <td>20130213</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "    user_id  birthday  gender\n",
       "0      2757  20130311       1\n",
       "1    415971  20121111       0\n",
       "2   1372572  20120130       1\n",
       "3  10339332  20110910       0\n",
       "4  10642245  20130213       0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_info_df = pd.read_csv('tianchi_mum_baby.csv')\n",
    "baby_info_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b55a065a-bbf5-458d-96a0-ca98a650adfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 953 entries, 0 to 952\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype\n",
      "---  ------    --------------  -----\n",
      " 0   user_id   953 non-null    int64\n",
      " 1   birthday  953 non-null    int64\n",
      " 2   gender    953 non-null    int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 22.5 KB\n"
     ]
    }
   ],
   "source": [
    "baby_info_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "68d201d7-d307-4a9d-9ac6-6d3c8f5c6c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19840616"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_info_df['birthday'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e73153c2-ed51-4326-b3b7-0c3423e5b7db",
   "metadata": {},
   "outputs": [
    {
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       "        user_id  birthday  gender\n",
       "97     89520261  19840616       0\n",
       "717    78923188  20021205       0\n",
       "946  1973092345  20030303       0\n",
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     "execution_count": 17,
     "metadata": {},
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   "source": [
    "baby_info_df.sort_values('birthday').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb180447-101b-4aa8-bbfc-e5cd6b721160",
   "metadata": {},
   "source": [
    "因为淘宝网是2003年才诞生的，网购的消费习惯多形成在2010年前后，因此我们剔除掉异常的生日数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "74ce33ce-1e00-4661-bd24-37eb06c711a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 824 entries, 0 to 952\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype\n",
      "---  ------    --------------  -----\n",
      " 0   user_id   824 non-null    int64\n",
      " 1   birthday  824 non-null    int64\n",
      " 2   gender    824 non-null    int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 25.8 KB\n"
     ]
    }
   ],
   "source": [
    "baby_info_df = baby_info_df[baby_info_df['birthday']>20100101]\n",
    "baby_info_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c4b303c1-062a-4b6f-89ec-b023159771fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29784"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trade_df.user_id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "606317c3-fe0c-4eaa-984f-c0795be92dad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 824 entries, 0 to 823\n",
      "Data columns (total 10 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   user_id     824 non-null    int64         \n",
      " 1   birthday    824 non-null    int64         \n",
      " 2   gender      824 non-null    int64         \n",
      " 3   auction_id  824 non-null    int64         \n",
      " 4   category_2  824 non-null    int64         \n",
      " 5   category_1  824 non-null    int64         \n",
      " 6   buy_mount   824 non-null    int64         \n",
      " 7   day         824 non-null    datetime64[ns]\n",
      " 8   Y           824 non-null    int64         \n",
      " 9   M           824 non-null    int64         \n",
      "dtypes: datetime64[ns](1), int64(9)\n",
      "memory usage: 64.5 KB\n"
     ]
    }
   ],
   "source": [
    "#查看提供了出生年月日的用户\n",
    "baby_user_df = pd.merge(baby_info_df,trade_df,how='inner',on='user_id')\n",
    "baby_user_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ae946ea7-eb5c-4439-8bd6-380e71f90c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    824.000000\n",
       "mean       1.382282\n",
       "std        1.762503\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        1.000000\n",
       "75%        1.000000\n",
       "max       26.000000\n",
       "Name: buy_mount, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_user_df['buy_mount'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c20ad97e-3f24-4648-8258-8873ae378071",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>20120328</td>\n",
       "      <td>0</td>\n",
       "      <td>5774771230</td>\n",
       "      <td>50006521</td>\n",
       "      <td>50014815</td>\n",
       "      <td>10</td>\n",
       "      <td>2013-01-11</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>20121104</td>\n",
       "      <td>1</td>\n",
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       "      <td>50006032</td>\n",
       "      <td>50022520</td>\n",
       "      <td>10</td>\n",
       "      <td>2012-10-07</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>315</th>\n",
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       "      <td>1</td>\n",
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       "      <td>50008817</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>348</th>\n",
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       "      <td>0</td>\n",
       "      <td>12881851566</td>\n",
       "      <td>50023790</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>2013-09-14</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
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      ],
      "text/plain": [
       "       user_id  birthday  gender   auction_id  category_2  category_1  \\\n",
       "410  299196791  20140331       0  38217159047    50013187          28   \n",
       "436  486110123  20140602       0  17491816827      250822          28   \n",
       "129  277629531  20130830       2  36579750374    50012437    50014815   \n",
       "660  178032355  20130510       1  17887907923    50011993          28   \n",
       "355  113554421  20100909       0  18263602472    50018831    50014815   \n",
       "43    57494340  20140518       0  20062826101    50012456    50014815   \n",
       "692  346883797  20120328       0   5774771230    50006521    50014815   \n",
       "686  306544399  20121104       1  14952161226    50006032    50022520   \n",
       "315   56736746  20121105       1  13725195879    50008817          28   \n",
       "348   94577459  20120706       0  12881851566    50023790          28   \n",
       "\n",
       "     buy_mount        day     Y   M  \n",
       "410         26 2014-05-21  2014   5  \n",
       "436         25 2013-05-11  2013   5  \n",
       "129         15 2014-08-25  2014   8  \n",
       "660         13 2013-01-21  2013   1  \n",
       "355         12 2013-05-14  2013   5  \n",
       "43          10 2014-03-08  2014   3  \n",
       "692         10 2013-01-11  2013   1  \n",
       "686         10 2012-10-07  2012  10  \n",
       "315         10 2013-01-05  2013   1  \n",
       "348         10 2013-09-14  2013   9  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_user_df.sort_values('buy_mount',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70c344f-2588-485c-9827-b88237569af8",
   "metadata": {},
   "source": [
    "### 数据分析阶段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "50dd11bb-4bb0-4877-a629-b697cbc24893",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "      Y   M  buy_mount\n",
       "0  2012   7        593\n",
       "1  2012   8        667\n",
       "2  2012   9        898\n",
       "3  2012  10        922\n",
       "4  2012  11        973"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_df_g = trade_df.groupby(['Y','M'])[['buy_mount']].sum()\n",
    "trade_df_g = trade_df_g.reset_index(drop=False)\n",
    "trade_df_g.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "77015ed9-c41b-4db6-8761-5be6ce8820d0",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1600x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "plt.subplot(2,1,1)\n",
    "sns.barplot(trade_df_g,x='M',y='buy_mount',hue='Y')\n",
    "plt.legend(loc='lower right', bbox_to_anchor=(1, 1))\n",
    "plt.subplot(2,1,2)\n",
    "sns.lineplot(trade_df_g,x='M',y='buy_mount',hue='Y',legend=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8b05da-99ac-45f0-99df-fab1ad3a92e4",
   "metadata": {},
   "source": [
    "2014年的同比增速为50.54%。那么这里可以把假定的问题翻译成具体的：2015年至今，销量同比增速低于目标的50.54%，需要将销量增速提至50.54%以上。?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "558c599f-bc1b-42ed-8b74-e9dbb2ad9b73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.195122\n",
       "1     1.114398\n",
       "2     0.491461\n",
       "3     0.621415\n",
       "4     0.517100\n",
       "5     0.557460\n",
       "6     0.428425\n",
       "7     0.455720\n",
       "8     0.368768\n",
       "9     0.528682\n",
       "10    0.544312\n",
       "11    0.461801\n",
       "Name: buy_mount, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_df_g = trade_df_g.sort_values('M')\n",
    "m14 = trade_df_g[trade_df_g['Y']==2014].reset_index(drop=True)\n",
    "m13 = trade_df_g[trade_df_g['Y']==2013].reset_index(drop=True)\n",
    "(m14['buy_mount']/m13['buy_mount']-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4cc52b3-ec98-4aa7-8ba5-473413cd4ec5",
   "metadata": {},
   "source": [
    "除了15年2月销量由于数据不全而出现骤降外，2013年的2月也同样出现了环比骤降的情况，第一反应是春节导致的下降。翻查13年-15年的春节（初一到初七）时间如下：\n",
    "- 2013年春节：2月9日-2月15日\n",
    "- 2014年春节：1月30日-2月6日\n",
    "- 2015年春节：2月19日-2月25日\n",
    "  \n",
    "可以得知，15年的春节时间与13年类似，都是完整分布在2月，可初步推出，15年2月的销量数据应该与13年类似。如果把15年2月的目标定为同比增长50%显然不尽合理，因此我们将时间线修改为春节前30天。\n",
    "\n",
    "观察数据日期：\n",
    "\n",
    "2013:  1月11日到2月8日\n",
    "2014： 1月1日到1月29日\n",
    "2015： 1月21日到2月5日(数据不全)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fc6374dd-4402-42a1-8a36-79990399d4d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = pd.to_datetime('20130111')\n",
    "d2 = pd.to_datetime('20130208')\n",
    "\n",
    "d3 = pd.to_datetime('20140101')\n",
    "d4 = pd.to_datetime('20140129')\n",
    "\n",
    "d5= pd.to_datetime('20150121')\n",
    "d6 = pd.to_datetime('20150205')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "84b0e0cb-3720-4581-ae07-9763b024b954",
   "metadata": {},
   "outputs": [],
   "source": [
    "m2013 = trade_df[(d1<trade_df.day)&(trade_df.day<d2)]\n",
    "m2014 = trade_df[(d3<trade_df.day)&(trade_df.day<d4)]\n",
    "m2015 = trade_df[(d5<trade_df.day)&(trade_df.day<d6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "22746ca1-b757-4863-b650-7a66018aedee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>auction_id</th>\n",
       "      <th>category_2</th>\n",
       "      <th>category_1</th>\n",
       "      <th>buy_mount</th>\n",
       "      <th>day</th>\n",
       "      <th>Y</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>288586293</td>\n",
       "      <td>10432932727</td>\n",
       "      <td>50016116</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-29</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>641175953</td>\n",
       "      <td>21904732463</td>\n",
       "      <td>50013636</td>\n",
       "      <td>50008168</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-29</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>546771881</td>\n",
       "      <td>17109662182</td>\n",
       "      <td>50003700</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-27</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>77125929</td>\n",
       "      <td>20131468178</td>\n",
       "      <td>50018831</td>\n",
       "      <td>50014815</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-27</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>197797522</td>\n",
       "      <td>14056558295</td>\n",
       "      <td>50016704</td>\n",
       "      <td>50022520</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-02-12</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id   auction_id  category_2  category_1  buy_mount        day  \\\n",
       "217  288586293  10432932727    50016116          38          1 2013-01-29   \n",
       "218  641175953  21904732463    50013636    50008168          1 2013-01-29   \n",
       "274  546771881  17109662182    50003700          28          1 2013-01-27   \n",
       "275   77125929  20131468178    50018831    50014815          2 2013-01-27   \n",
       "555  197797522  14056558295    50016704    50022520          1 2013-02-12   \n",
       "\n",
       "        Y  M  \n",
       "217  2013  1  \n",
       "218  2013  1  \n",
       "274  2013  1  \n",
       "275  2013  1  \n",
       "555  2013  2  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2013.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "68f27559-a50f-45e3-b161-0f5468f4151d",
   "metadata": {},
   "outputs": [],
   "source": [
    "m2013g = m2013.groupby('day')[['buy_mount']].sum().reset_index(drop=False).reset_index(drop=False)\n",
    "m2014g = m2014.groupby('day')[['buy_mount']].sum().reset_index(drop=False).reset_index(drop=False)\n",
    "m2015g = m2015.groupby('day')[['buy_mount']].sum().reset_index(drop=False).reset_index(drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "23b37cee-5b0f-479b-9f5d-6f2dd569373c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>day</th>\n",
       "      <th>buy_mount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2013-01-13</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-14</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-15</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-01-16</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-01-17</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        day  buy_mount\n",
       "0      0 2013-01-13         24\n",
       "1      1 2013-01-14         24\n",
       "2      2 2013-01-15         22\n",
       "3      3 2013-01-16         33\n",
       "4      4 2013-01-17         22"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2013g.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bcc86dbb-3165-4879-bd75-9b88bf7bd225",
   "metadata": {},
   "outputs": [],
   "source": [
    "m2013g['year']=2013\n",
    "m2014g['year']=2014\n",
    "m2015g['year']=2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9407b7ef-a1c5-45d9-a4b1-5d4a46d09d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_mg_df = pd.concat([m2013g,m2014g,m2015g],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "eb34774e-67a2-41eb-86e4-1ef47e6b289c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.barplot(all_mg_df,x='index',y = 'buy_mount',hue='year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbeda32a-715e-46e4-ae9b-f4d8ba7ac952",
   "metadata": {},
   "source": [
    "看前14天的总销量情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ad4179bb-91db-4115-a701-937138ce0d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>day</th>\n",
       "      <th>buy_mount</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2013-01-13</td>\n",
       "      <td>24</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-14</td>\n",
       "      <td>24</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-15</td>\n",
       "      <td>22</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-01-16</td>\n",
       "      <td>33</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-01-17</td>\n",
       "      <td>22</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        day  buy_mount  year\n",
       "0      0 2013-01-13         24  2013\n",
       "1      1 2013-01-14         24  2013\n",
       "2      2 2013-01-15         22  2013\n",
       "3      3 2013-01-16         33  2013\n",
       "4      4 2013-01-17         22  2013"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_mg_df_14  = all_mg_df[all_mg_df['index']<14]\n",
    "all_mg_df_14.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a1549ce0-b954-49f1-904a-0fc02a6d72a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>buy_mount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  buy_mount\n",
       "0  2013        402\n",
       "1  2014        650\n",
       "2  2015        951"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_mg_df_14g = all_mg_df_14.groupby('year')['buy_mount'].sum().reset_index()\n",
    "all_mg_df_14g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ee3c3ffe-7941-4284-8fb0-e9ecd2141043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>buy_mount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>1067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  buy_mount\n",
       "0  2013        564\n",
       "1  2014       1067\n",
       "2  2015       1011"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_mg_df.groupby('year')['buy_mount'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d59babf-0399-42a4-8757-763f76a46e9f",
   "metadata": {},
   "source": [
    "前两周同比增长"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5c48710c-7e66-4249-b7fa-cc7fb2a28a27",
   "metadata": {},
   "outputs": [],
   "source": [
    "trade_df_cat = trade_df[trade_df['day'].apply(lambda x:x in all_mg_df_14.day.values)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "84da65d8-d189-4fe9-bf40-e34e84671849",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>category_2</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
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       "      <td>50015841</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-02-03</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id   auction_id  category_2  category_1  buy_mount        day  \\\n",
       "27   763560371  40945285800    50012365   122650008          1 2015-02-01   \n",
       "51   115566151  14778919435    50013187          28          1 2014-01-13   \n",
       "77   151915451  17305821144      211122          38          2 2014-01-04   \n",
       "78   745002413  36815797313    50023645          28          1 2014-01-04   \n",
       "95  1097191176  39095838474    50015841          28          1 2015-02-03   \n",
       "\n",
       "       Y  M  \n",
       "27  2015  2  \n",
       "51  2014  1  \n",
       "77  2014  1  \n",
       "78  2014  1  \n",
       "95  2015  2  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_df_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b6da7c2f-8212-4975-b02c-06b324459552",
   "metadata": {},
   "outputs": [
    {
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       "      <th>16</th>\n",
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       "      <td>50022520</td>\n",
       "      <td>43</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015</td>\n",
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      ],
      "text/plain": [
       "       Y  category_1  buy_mount\n",
       "0   2013          28        128\n",
       "1   2013          38         15\n",
       "2   2013    50008168        140\n",
       "3   2013    50014815         73\n",
       "4   2013    50022520         19\n",
       "5   2013   122650008         27\n",
       "6   2014          28        149\n",
       "7   2014          38         61\n",
       "8   2014    50008168        248\n",
       "9   2014    50014815        118\n",
       "10  2014    50022520         29\n",
       "11  2014   122650008         45\n",
       "12  2015          28        317\n",
       "13  2015          38        103\n",
       "14  2015    50008168        286\n",
       "15  2015    50014815        146\n",
       "16  2015    50022520         43\n",
       "17  2015   122650008         56"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_df_cat_g = trade_df_cat.groupby(['Y','category_1'])[['buy_mount']].sum().reset_index()\n",
    "trade_df_cat_g "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f0c17b0a-fbaa-43be-9de8-42604bcc3919",
   "metadata": {},
   "outputs": [],
   "source": [
    "m2013_14d = trade_df_cat_g[trade_df_cat_g['Y']==2013].set_index('category_1')\n",
    "m2014_14d = trade_df_cat_g[trade_df_cat_g['Y']==2014].set_index('category_1')\n",
    "m2015_14d = trade_df_cat_g[trade_df_cat_g['Y']==2015].set_index('category_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "801f10be-a906-4f7b-81b0-b390e502e2f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = m2013_14d[['buy_mount']]\n",
    "b =  m2014_14d[['buy_mount']]\n",
    "c =  m2015_14d[['buy_mount']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "39f6a718-8b5e-4fa9-aa33-4def863736da",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_df = ((b-a)/a).reset_index()\n",
    "res_df['year'] = 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1f67608c-0fb8-4749-af9d-f83386135dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_df = ((c-b)/b).reset_index()\n",
    "temp_df['year'] = 2015\n",
    "res_df = pd.concat([res_df,temp_df],ignore_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "153eff6a-1a7e-4b55-91e1-bbbec20057e4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.127517</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>0.688525</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.153226</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50014815</td>\n",
       "      <td>0.237288</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50022520</td>\n",
       "      <td>0.482759</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>122650008</td>\n",
       "      <td>0.244444</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category_1  buy_mount  year\n",
       "0         28   0.164062  2014\n",
       "1         38   3.066667  2014\n",
       "2   50008168   0.771429  2014\n",
       "3   50014815   0.616438  2014\n",
       "4   50022520   0.526316  2014\n",
       "5  122650008   0.666667  2014\n",
       "0         28   1.127517  2015\n",
       "1         38   0.688525  2015\n",
       "2   50008168   0.153226  2015\n",
       "3   50014815   0.237288  2015\n",
       "4   50022520   0.482759  2015\n",
       "5  122650008   0.244444  2015"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df['category_1'] = res_df['category_1'].astype('str')\n",
    "res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ebe72494-01f1-47ba-a5f2-019ba86edfb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import seaborn as sns\n",
    "# import matplotlib.pyplot as plt\n",
    " \n",
    "# # ax1 for KDE, ax2 for CDF\n",
    " \n",
    "# f, ax1 = plt.subplots()\n",
    "# ax1.grid(True)\n",
    "# # ax1.set_ylim(0, 1)\n",
    "# ax1.set_ylabel('KDE')\n",
    "# ax1.set_xlabel('DATA')\n",
    "# ax1.set_title('KDE + CDF')\n",
    "# ax1.legend(loc=2)\n",
    "# sns.kdeplot(data, ax=ax1, lw=2, label='KDE') # KDE\n",
    " \n",
    "# ax2 = ax1.twinx() # the reason why it works\n",
    "# ax2.set_ylabel('CDF')\n",
    "# ax2.legend(loc=1)\n",
    "# ax2.hist(data, bins=50, cumulative=True, normed=True, histtype='step', color='red', lw=2, label='CDF') # CDF\n",
    " \n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b1868f44-a13e-4460-880b-dfc6835a6549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax1 = plt.subplots()\n",
    "sns.barplot(trade_df_cat_g,x='category_1',y='buy_mount',hue='Y',ax=ax1)\n",
    "ax1.legend(loc=(0.8,0.65))\n",
    "ax2 = ax1.twinx()\n",
    "sns.lineplot(res_df,x='category_1',y='buy_mount',hue='year',ax=ax2)\n",
    "ax2.set_ylabel('YOY')\n",
    "ax2.legend(loc=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbe0ad72-da59-46f6-a974-3eaf612351c2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
